library(highcharter)
library(forecast)
library(dplyr)
library(quantmod)
library(modeltime)tickers <- readxl::read_xlsx('data_input/Yahoo Ticker Symbols - September 2017.xlsx', sheet = 'Stock', col_names = FALSE)[-(1:4), c(1,2)]colnames(tickers) <- c('ticker_', 'name')
ticker <- 'BBCA.JK'
name <- tickers %>% filter(ticker_ == ticker) %>% pull(name) %>% toString()
ticker## [1] "BBCA.JK"name## [1] "PT Bank Central Asia Tbk"df <- getSymbols(ticker, auto.assign= FALSE)
head(df)## BBCA.JK.Open BBCA.JK.High BBCA.JK.Low BBCA.JK.Close BBCA.JK.Volume
## 2007-01-02 2625 2625 2575 2625 15400000
## 2007-01-03 2575 2625 2575 2575 23496000
## 2007-01-04 2600 2600 2550 2600 26830000
## 2007-01-05 2600 2600 2575 2600 14040000
## 2007-01-08 2625 2625 2575 2625 27296000
## 2007-01-09 2600 2625 2550 2600 34384000
## BBCA.JK.Adjusted
## 2007-01-02 2044.696
## 2007-01-03 2005.749
## 2007-01-04 2025.223
## 2007-01-05 2025.223
## 2007-01-08 2044.696
## 2007-01-09 2025.223tail(df)## BBCA.JK.Open BBCA.JK.High BBCA.JK.Low BBCA.JK.Close BBCA.JK.Volume
## 2020-08-07 31325 31425 30800 30900 8724200
## 2020-08-10 30900 31000 30525 30600 7554900
## 2020-08-11 30700 31000 30650 30875 7759100
## 2020-08-12 30875 31300 30800 31300 7122400
## 2020-08-13 31300 31500 30900 31500 9716200
## 2020-08-14 31500 32025 31200 32025 11463200
## BBCA.JK.Adjusted
## 2020-08-07 30900
## 2020-08-10 30600
## 2020-08-11 30875
## 2020-08-12 31300
## 2020-08-13 31500
## 2020-08-14 32025summary(df)## Index BBCA.JK.Open BBCA.JK.High BBCA.JK.Low
## Min. :2007-01-02 Min. : 2100 Min. : 2225 Min. : 2000
## 1st Qu.:2010-06-17 1st Qu.: 5750 1st Qu.: 5850 1st Qu.: 5650
## Median :2013-11-21 Median :10550 Median :10700 Median :10450
## Mean :2013-11-13 Mean :12613 Mean :12739 Mean :12481
## 3rd Qu.:2017-05-02 3rd Qu.:17500 3rd Qu.:17700 3rd Qu.:17300
## Max. :2020-08-14 Max. :34750 Max. :35300 Max. :34175
## NA's :8 NA's :8 NA's :8
## BBCA.JK.Close BBCA.JK.Volume BBCA.JK.Adjusted
## Min. : 2025 Min. : 0 Min. : 1715
## 1st Qu.: 5750 1st Qu.: 8804500 1st Qu.: 4951
## Median :10600 Median : 13200700 Median : 9590
## Mean :12614 Mean : 16542994 Mean :11845
## 3rd Qu.:17525 3rd Qu.: 19755100 3rd Qu.:16722
## Max. :34375 Max. :217114000 Max. :33791
## NA's :8 NA's :8 NA's :8We have some missing values, lets check where are they.
df %>% subset(is.na(BBCA.JK.Open))## BBCA.JK.Open BBCA.JK.High BBCA.JK.Low BBCA.JK.Close BBCA.JK.Volume
## 2008-10-09 NA NA NA NA NA
## 2008-10-10 NA NA NA NA NA
## 2008-12-31 NA NA NA NA NA
## 2009-04-09 NA NA NA NA NA
## 2009-05-21 NA NA NA NA NA
## 2009-07-08 NA NA NA NA NA
## 2009-07-20 NA NA NA NA NA
## 2011-07-04 NA NA NA NA NA
## BBCA.JK.Adjusted
## 2008-10-09 NA
## 2008-10-10 NA
## 2008-12-31 NA
## 2009-04-09 NA
## 2009-05-21 NA
## 2009-07-08 NA
## 2009-07-20 NA
## 2011-07-04 NASince this is time series data, I will use time series interpolation to fill the missing values
impute_ts <- function(x){
x <- ts(x, 1, length(x))
na.interp(x)
}
# function testing
df[, 1] %>% impute_ts() %>% summary()## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2100 5700 10550 12592 17500 34750df[, 2] %>% impute_ts() %>% summary()## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2225 5800 10700 12718 17650 35300impute_df <- function(df){
for (i in 1:length(df[1,])){
df[, i] <- df[, i] %>% impute_ts()
}
df
}
df %>% impute_df() %>% summary()## Index BBCA.JK.Open BBCA.JK.High BBCA.JK.Low
## Min. :2007-01-02 Min. : 2100 Min. : 2225 Min. : 2000
## 1st Qu.:2010-06-17 1st Qu.: 5700 1st Qu.: 5800 1st Qu.: 5600
## Median :2013-11-21 Median :10550 Median :10700 Median :10450
## Mean :2013-11-13 Mean :12592 Mean :12718 Mean :12460
## 3rd Qu.:2017-05-02 3rd Qu.:17500 3rd Qu.:17650 3rd Qu.:17250
## Max. :2020-08-14 Max. :34750 Max. :35300 Max. :34175
## BBCA.JK.Close BBCA.JK.Volume BBCA.JK.Adjusted
## Min. : 2025 Min. : 0 Min. : 1715
## 1st Qu.: 5700 1st Qu.: 8821500 1st Qu.: 4931
## Median :10600 Median : 13200700 Median : 9562
## Mean :12593 Mean : 16548159 Mean :11825
## 3rd Qu.:17475 3rd Qu.: 19784000 3rd Qu.:16651
## Max. :34375 Max. :217114000 Max. :33791Applying the imputation function
df <- df %>% impute_df()tail(RSI(df[ ,4]))## Warning: timezone of object (UTC) is different than current timezone ().## rsi
## 2020-08-07 56.12723
## 2020-08-10 52.38342
## 2020-08-11 55.32513
## 2020-08-12 59.49037
## 2020-08-13 61.31810
## 2020-08-14 65.69372tail(BBands(df[, 2:4]))## Warning: timezone of object (UTC) is different than current timezone ().## dn mavg up pctB
## 2020-08-07 30387.86 30806.25 31224.64 0.7813349
## 2020-08-10 30378.82 30798.33 31217.84 0.3927321
## 2020-08-11 30378.13 30794.17 31210.20 0.5570864
## 2020-08-12 30367.12 30808.33 31249.54 0.8683050
## 2020-08-13 30338.71 30828.33 31317.96 0.9816604
## 2020-08-14 30238.84 30872.50 31506.16 1.1924012tail(MACD(df[, 4]))## Warning: timezone of object (UTC) is different than current timezone ().## macd signal
## 2020-08-07 1.516167 1.818648
## 2020-08-10 1.328539 1.720626
## 2020-08-11 1.237662 1.624033
## 2020-08-12 1.262187 1.551664
## 2020-08-13 1.318129 1.504957
## 2020-08-14 1.481010 1.500168short_ma_ser <- SMA(df[, 4], 20)
long_ma_ser <- SMA(df[, 4], 100)
hcPlotStock <- function(df, ticker_name, ticker, show_ma = TRUE, show_bbands = FALSE, show_volume = TRUE, show_macd = FALSE, show_rsi = TRUE, short_ma = short_ma_ser, long_ma = long_ma_ser){
# colnames(df) <- c('Open', 'High', 'Low', 'Close', 'Volume', 'Adjusted')
n_axis <- show_volume + show_macd + show_rsi + 1
height_axis <- c(2, seq(1, 1, length.out = n_axis - 1))
y_macd <- 1 + show_volume
y_rsi <- 1 + show_volume + show_macd
hcChart <- highchart(type = 'stock') %>%
# create axis
hc_yAxis_multiples(create_yaxis(n_axis, height = height_axis, turnopposite = TRUE)) %>%
# decimals setting
hc_tooltip(valueDecimals = 2)
if(show_volume){
hcChart <- hcChart %>%
## Add volume
hc_add_series(df[, 5], color = "orange", yAxis = 1, name = "Volume", type = "column", showInLegend = FALSE)
}
if(show_macd){
# calculate MACD
macd <- MACD(df[, 4], 12, 26, 9)
macd$diff <- macd$macd - macd$signal
diff.high <- ifelse(macd$diff > 0, macd$diff, 0)
diff.low <- ifelse(macd$diff < 0, macd$diff, 0)
hcChart <- hcChart %>%
# set Bar Chart Stacking Option
hc_plotOptions(column = list(stacking = "normal")) %>%
## Add MACD
hc_add_series(diff.high, yAxis = y_macd, name = 'MACD diff', type = 'column', color = 'green', showInLegend = FALSE, enableMouseTracking = FALSE, stack = 'diff') %>%
hc_add_series(diff.low, yAxis = y_macd, name = 'MACD diff', type = 'column', color = 'red', showInLegend = FALSE, enableMouseTracking = FALSE, stack = 'diff') %>%
hc_add_series(macd$macd, yAxis = y_macd, name = 'MACD', color = 'blue', showInLegend = FALSE) %>%
hc_add_series(macd$signal, yAxis = y_macd, name = 'Signal Line', color = 'orange', showInLegend = FALSE)
}
if(show_rsi){
hcChart <- hcChart %>%
## RSI
hc_add_series(RSI(df[, 4]), yAxis = y_rsi, name = "Relative Stregth Index", color = hex_to_rgba("green", 0.7), showInLegend = FALSE) %>%
hc_add_series(xts(rep(70, NROW(df)), index(df)), color = hex_to_rgba("red", 0.7), yAxis = y_rsi, name = "Sell level", enableMouseTracking = FALSE, dashStyle = 'LongDash') %>%
hc_add_series(xts(rep(30, NROW(df)), index(df)), color = hex_to_rgba("blue", 0.7), yAxis = y_rsi, name = "Buy level", enableMouseTracking = FALSE, dashStyle = 'LongDash') %>%
# decimals setting
hc_tooltip(valueDecimals = 2)
}
if(show_ma){
hcChart <- hcChart %>%
## Moving Averages
hc_add_series(short_ma, yAxis = 0, name = 'Short Moving Average', color = 'seagreen', enableMouseTracking = FALSE) %>%
hc_add_series(long_ma, yAxis = 0, name = 'Long Moving Average', color = 'skyblue', enableMouseTracking = FALSE)
}
if(show_bbands){
# calculate Bollinger bands position
bbands <- BBands(df[,2:4])
hcChart <- hcChart %>%
## Add Bollinger Bands
hc_add_series(bbands[, 'up'], yAxis = 0, name = 'Bollinger Bands', color = '#ccc', enableMouseTracking = FALSE, showInLegend = FALSE) %>%
hc_add_series(bbands[, 'dn'], yAxis = 0, name = 'Bollinger Bands', color = '#ccc', enableMouseTracking = FALSE) %>%
hc_add_series(bbands[, 'mavg'], yAxis = 0, name = 'Moving Average (20)', color = '#E377C2', enableMouseTracking = FALSE)
}
hcChart %>%
# add title
hc_title(text = paste(ticker, ticker_name, sep = ' - '), align = 'left', x = 30) %>%
# add series
## candlechart
hc_add_series(df, yAxis = 0, name = ticker, showInLegend = FALSE) %>%
# add legend
hc_legend(enabled = TRUE, align = 'center') %>%
# set theme
hc_add_theme(hc_theme_db())
}
hcPlotStock(df, name, ticker)hcPlotStock(df, name, ticker, FALSE, TRUE, TRUE, TRUE, TRUE)hcPlotStock(df, name, ticker, TRUE, FALSE, FALSE, TRUE, FALSE)hcPlotStock(df, name, ticker, FALSE, FALSE, FALSE, TRUE, TRUE)